Multi-Object Tracking Algorithm Based on Motion Estimation and Appearance Adaptive Matching

With the increasing application of multi-object tracking in practical scenarios, the problem of tracking object loss due to occlusion is receiving more and more attention. However, multi-object tracking techniques still face significant challenges, and the accuracy and robustness of tracking need to be further improved. To address these problems, a multi-object tracking algorithm based on motion estimation and adaptive appearance matching is proposed, using BoT-SORT as a baseline. First, a method called ME-DIOU is proposed to compute the similarity of targets based on motion estimation. This method improves the accuracy of matching tracked targets in successive frames when there is a loss of appearance features. Secondly, an adaptive thresholding method for appearance feature matching is proposed to improve the stability of appearance models in different scenes. Experiments were conducted on public datasets and the results show that the improved algorithm has better HOTA, MOTA and IDF1 values compared to the BoT-SORT algorithm. Compared to similar algorithms, the improved algorithm has more stable performance in dealing with occlusion and other problems, reducing the risk of losing tracking targets after occlusion.